An Approach for Automatic Detection of InSAR Deformation Signals
Associated with Wastewater Injection and Induced Seismic Events
Abstract
Since 2008, the rate of seismic events within the Central United States
has dramatically increased, which is likely associated with wastewater
injection from nearby oil and gas operations. Surface deformation
measurements derived from spaceborne interferometric synthetic aperture
radar (InSAR) data can be used to quantify the magnitude and spatial
extent of the injection-related stress perturbation, which are critical
for understanding the complex interaction between the injected fluid and
the earth’s subsurface. In this study, we processed Sentinel-1 InSAR
data over Central and West Texas using a recently developed processing
framework that performs topography/geometry phase corrections prior to
the interferogram formation (Zebker 2017). We streamlined the creation
of upsampled digital elevation maps (DEMs) from NASA Shuttle Radar
Topographic Mission (SRTM) data, as well as the collection of Sentinel-1
precise orbit data. We developed a tool for InSAR time-series analysis
and data visualization. To detect unknown deformation signatures from
large volumes of InSAR data, we employed computer vision ideas for
feature detection independent of scale, well known through their success
in the Scale Invariant Feature Transform (SIFT). We used multi-scale
Laplacian-of-Gaussian (LoG) filters to find local maxima and minima in a
coarse deformation solution, corresponding to “bowls” of uplift and
subsidence, respectively. This allowed us to drastically cut down
processing time of high-resolution InSAR products. As a validation, our
method successfully detected all sinkhole locations, injection-related
uplift signals and production-related subsidence signals as reported in
Kim and Lu (2017) over a 100 km x 100km search area without the need for
manual inspection. We then examined the Dallas Fort Worth Basin area for
evidence of deformation near wastewater injection and oil/gas production
sites. We begin to quantify the uncertainty from common noise sources to
produce more confident time-series results.